In below code of pipeline. Even though i have encoded the sex column, i am getting string to float error.

from sklearnpose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np

# Step 1: Imputation
trf1 = ColumnTransformer([
    ('impute_age', SimpleImputer(), [2]),  # Impute Age
    ('impute_embarked', SimpleImputer(strategy='most_frequent'), [6])  # Impute Embarked
], remainder='passthrough')

# Step 2: One-Hot Encoding
trf2 = ColumnTransformer([
    ('onehot_sex_embarked', OneHotEncoder(sparse_output=False, handle_unknown='ignore'), [1, 6])  # Encode Sex and Embarked
], remainder='passthrough')

# Step 3: Scaling
trf3 = ColumnTransformer([
    ('scale', MinMaxScaler(), slice(0, None))  # Scale all columns
], remainder='passthrough')

# Step 4: Classifier
trf4 = DecisionTreeClassifier()

# Create pipeline
pipe = Pipeline([
    ('trf1', trf1),  # Step 1: Imputation
    ('trf2', trf2),  # Step 2: One-hot encoding
    ('trf3', trf3),  # Step 3: Scaling
    ('trf4', trf4)   # Step 4: Model
])

# Ensure proper ha
# Fit the pipeline
pipe.fit(X_train, y_train)

Error:

What is the reason to the error?

In below code of pipeline. Even though i have encoded the sex column, i am getting string to float error.

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np

# Step 1: Imputation
trf1 = ColumnTransformer([
    ('impute_age', SimpleImputer(), [2]),  # Impute Age
    ('impute_embarked', SimpleImputer(strategy='most_frequent'), [6])  # Impute Embarked
], remainder='passthrough')

# Step 2: One-Hot Encoding
trf2 = ColumnTransformer([
    ('onehot_sex_embarked', OneHotEncoder(sparse_output=False, handle_unknown='ignore'), [1, 6])  # Encode Sex and Embarked
], remainder='passthrough')

# Step 3: Scaling
trf3 = ColumnTransformer([
    ('scale', MinMaxScaler(), slice(0, None))  # Scale all columns
], remainder='passthrough')

# Step 4: Classifier
trf4 = DecisionTreeClassifier()

# Create pipeline
pipe = Pipeline([
    ('trf1', trf1),  # Step 1: Imputation
    ('trf2', trf2),  # Step 2: One-hot encoding
    ('trf3', trf3),  # Step 3: Scaling
    ('trf4', trf4)   # Step 4: Model
])

# Ensure proper ha
# Fit the pipeline
pipe.fit(X_train, y_train)

Error:

What is the reason to the error?

• python
• pandas
• scikit-learn
• scikit-learn-pipeline

• 2 Maybe the column numbers should be offset by 1 but more information is needed: the first few lines of X_train and the error traceback. – rehaqds Commented Jan 19 at 13:26
• More details are indeed needed, but from the comments in the code I notice that you expect Embarked to be column number 6 both before and after the imputation step. It won't be. See e.g. stackoverflow.com/q/62225230/10495893 and its Linked pages – Ben Reiniger Commented Jan 19 at 14:32

1 Answer 1

It is generally not a very good idea to use multiple ColumnTransformer() stages in a row as the column index control becomes tedious, if even possible.

I'd advise wrapping transforms into a Pipeline() for each transformation group and thus sticking to a single ColumnTransformer(). Wrapping a scaler into a ColumnTransformer() is also redundant (and the effect of a scaler on a tree model is likely insignificant).

Revised code, assuming the titanic column naming:

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np

# Step 1: Preprocessing
trf1 = ColumnTransformer(
    [
        ('impute', SimpleImputer(), ['Age']),
        ('impute_and_encode', Pipeline([
            ('impute', SimpleImputer(strategy='most_frequent')),
            ('encode', OneHotEncoder(sparse_output=False, handle_unknown='ignore', drop='first')),
        ]), ['Embarked']),
        ('encode', OneHotEncoder(sparse_output=False, handle_unknown='ignore', drop='first'), ['Sex']),
    ],
    remainder='passthrough',
    force_int_remainder_cols=False,
)

# Step 2: Scaling
trf2 = MinMaxScaler()

# Step 3: Classifier
trf3 = DecisionTreeClassifier(random_state=177013)

# Create pipeline
pipe = Pipeline([
    ('trf1', trf1),  # Step 1: Preprocessing
    ('trf2', trf2),  # Step 2: Scaling
    ('trf3', trf3)   # Step 3: Model
])

# Fit the pipeline
pipe.fit(X_train, y_train)